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TRENDY EKONOMIKY A MANAGEMENTU / TRENDS ECONOMICS AND MANAGEMENT
points (last couple and first couple of days and
in the month) generate higher returns than other
days of the month.
Looking for time-of-the-year anomalies
Brown and Luo (2004) tested monthly
seasonality in stock market returns found that
January has a characteristic to predict a
tendency for the year. Their tests show that “if
an investor wishes to invest in the stock market
for one calendar month only, he or she is better
off being in the stock market during January
than during any other calendar month“. They
also concluded that when stock prices rise (fall)
in a particular calendar month, they rise by
more (fall by less) in January than they do in
any other calendar month so the January effect
works both conditionally and unconditionally.
One of the most interesting
phenomenon found in stock exchange is called
“Sell in May and Go Away” effect. Bouman
and Jacobsen (2002) revealed that a trading
strategy of tactical asset allocation based on the
old saying “Sell in May and go away”
generated abnormal returns in comparison with
stock market indices in most countries in their
study. They tried to find the explanation for this
anomaly by testing if various popular
hypotheses show any scientifically significant
evidence. They tested if January effect causes
this anomaly or it is only sector-specific
anomaly and also if trading volume and interest
rates during that period showing some
divergence with other months of the year.
These tests did not explained this anomaly, but
one thing they did find was that “the size of the
effect is significantly related to both length and
timing of vacations and also to the impact of
vacations on trading activity in different
countries”.
2 Data and metodology
The monthly total returns for analysis
of Lithuanian stock market are taken from
Nasdaq OMXV index. The closing bid-ask
quotes are obtained from the NasdaqOMX
database, which is available from January 2000.
Accordingly, our study covers 2000 through
March 2010.
We used the natural logarithm of one
plus these returns in our analysis. A shortened
version of the X-11 procedure (Dagum, 1980)
is applied to the returns data from January 2000
to March 2010 for estimating the seasonal
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ROČNÍK IV – ČÍSLO 06 / VOLUME IV – NUMBER 06
components. The basic model used in our study
is the traditional three-component additive
version:
where
ln[l + R(t)] = SF(t) + TC(t) + IR(t) (1)
R(t) = total return during the month t;
SF(t) = the seasonal component of ln[l + R(t)];
TC(t) = the nonseasonal systematic component of
ln[l + R(t)], commonly referred to as the trendcyclical
component;
IR(t) = the irregular component of ln[l + R(t)].
It is assumed that both SF and TC move
stochastically but gradually through time. Thus,
for example, the seasonal components of
January returns across years could vary due to
changes in their expected values and/or
seasonal noises. The irregular component
represents random variation other than that
generated ty the stochastic movement of SF and
TC. It is assumed that the seasonal and the
trend-cyclical noises are not correlated with the
irregular component either contemporaneously
or temporally.
Return seasonality exists if the expected
values of the seasonal components of at least
two calendar months differ. A positive
(negative) seasonal component, also known as a
seasonal high (low), indicates positive
(negative) seasonal pressure or higher (lower)
stock returns than what would have been if
there were no special influence of events which
repeatedly occur in that month. In other words,
there is predictable surge in demand for stocks
in some month(s) which, however, is balanced
by slack in demand in some other month(s).
The surge and slack in demand are relative to
the average situation over the year. Hence, a
seasonally neutral month (zero seasonal
component) represents the average situation.
The economic meaning of the X-11
systematic and seasonal components should
thus be clear. The return one can expect during
a calendar month is equal to the sum of
expected TC and expected SF for that month;
we refer to the sum as the systematic
component of return. Since TC is expected to
be the same for all months over an extended
period of time, the average seasonal component
of a month would represent the difference
between it's expected (unconditional) return or